Radar Esm With A What-And-Where Fusion Neural Network

A neural network recognition and tracking system is proposed for classification of radar pulses in autonomous Electronic Support Measure systems. Radar type information is combined with position-specific information from active emitters in a scene. Such a What-and-Where fusion strategy is motivated by a similar subdivision of labor in the brain. OVERVIEW A critical function of radar Electronic Support Measures (ESM) [1, 2, 3] is the real-time identification of the radar type associated ith each pulse train that is intercepted. In this paper, a ne approach to this task is examined. Type-specific parameters of the input pulse stream are used to classify pulses according to radar type, hile environment-specific parameters are used to separate pulses corresponding to active emitters. An ESM system incorporating a neural net ork recognition system is depicted in Figure 1. First a time of arrival (TOA) deinterleaver uncovers periodicities in the TOA of input pulse description ords (PDWs). Whenever grouping of pulses is straightfor ard, it forms tracks and assigns a track number and a pulse repetition interval (PRI) to each grouped pulse. The neural net ork recognition system receives all the PDWs, some of hich have track numbers and PRI parameters. The neural net ork outputs a prediction of the radar type for every PDW, and assigns a track number to the PDWs that did not get one from the TOA deinterleaver